Expression pharmacogenomics uses comprehensive differential gene or protein expression profiling to describe drug response in selected model systems, usually with the goal of understanding how drugs exert both therapeutic and toxic effects. Two fundamental principles of expression pharmacogenomics underlie the present invention. The first principle is that each tissue can be characterized by the subset of genes expressed in its cells. This principle holds true for disease states, which can be characterized by disease-specific gene expression profiles. For example, colon cancer cells express a set of genes distinct from those expressed in normal colon cells or other cell types. The differences between the expression profile of disease and normal tissue can be considered a measure of the pathology of the diseased tissue. The second principle is that toxic and therapeutic responses to drugs can be characterized at the molecular level by the set of genes that are perturbed, or differentially regulated, from the normal baseline level of expression. Drugs with therapeutic action on diseased tissue can induce a stereotyped change in the disease's diagnostic gene expression profile. The types of genes that are affected can give insight into the mechanism of the drug, and the induced pattern of expression can serve as a “fingerprint” of the drug's action. Thus, expression pharmacogenomics yields gene expression patterns that are a surrogate measure of tissue physiology or of a compound's therapeutic toxic or biological effect.
A useful technique for the initial identification of drug candidates is high throughput screening of large collections of chemicals, often referred to as “libraries”. Most high-throughput screens measure the action of compounds on a single molecular phenomenon, e.g., a particular enzymatic activity that is thought to play a role in some physiological system such as a disease state. Prior to the screening process, the components of such libraries have not been demonstrated to have action on the molecular phenomenon measured by the screen or the disease state in which the molecular phenomena plays a role. Such a screen is designed to identify compounds that affect that particular molecular phenomenon, so that the physiological system in which the phenomena plays a role may be impinged upon with the identified compounds. Previously uncharacterized chemicals that exhibit a specific biochemical activity revealed by the screen are reclassified as “candidate drugs”, also known as “hits”, “drug candidates” and “drug leads”. Such newly-identified candidate drugs subsequently proceed through the drug development pipeline which includes the process of “triage”, where candidate drugs are subjected to further characterization and analysis to rank the candidates in order of likely efficacy and toxicity.
This approach has a number of inherent deficiencies. For example, a molecular phenomenon that is a crucial mediator of the physiological system of interest must first be known in order to design a specific screen for agents that affect that phenomenon. Much difficult laboratory research is often required to identify the mechanistic underpinnings of a physiological system of interest. Moreover, the mechanistic molecular phenomenon must lend itself to detection by a screen. Often, devising a detection strategy that is a direct indicator of the molecular phenomenon is impractical with existing technologies available to high throughput screening applications. Another limitation is that compounds that affect the physiological system of interest by some other mechanism than the molecular phenomenon at the heart of the screen are missed, due to the inherent specificity of the screen. Also, compounds identified by the screen may have unknown, undesirable side effects, due to undetected actions on other biological molecular phenomena (i.e., the compound acts nonspecifically on other molecular phenomenon not measured by the screen). Consequently, the overall physiological system can be modified in undesirable and unforeseen ways by compounds identified in the screen. These side effects must be subsequently detected and triaged through costly and inconvenient additional characterization. Another disadvantage lies in that the molecular phenomenon being measured may not be the ideal mediator of the physiological system sought to be influenced (i.e., the target of the screen may not really be a good target). Since the molecular phenomenon at the heart of the screen is only one part of a complex system of which all the component molecular phenomena are usually not known, even a compound that perfectly specifically targets the metric of the screen may not result in the desired final effect on the relevant physiological state.
Gene microarrays are efficient for high throughput triaging of many drug treated samples against a pre-defined set of interesting genes. Expression pharmacogenomics has been used to identify toxicity of previously derived drug leads (Rothberg et al. 2000).
Drug leads have never been derived from a high-throughput screen that uses the gene expression profile as the primary criteria for initial identification of a drug candidate.